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This article introduces how to carry out the development and predicament analysis of big data test. The content is very detailed. Interested friends can use it for reference. I hope it will be helpful to you.

With the in-depth development of big data technology, big data test came into being. It can be predicted that big data testing will become one of the development goals of software test engineers. Perhaps for many people, big data test is still a very unfamiliar concept. In fact, big data testing is different from traditional software testing in terms of testing types, strategies and tools. The following will give you a detailed analysis of the development and dilemma of big data test.

1. What is the big data test?

Testing big data's application is more about verifying its data processing than testing individual features of software products. In big data testing, performance and functional testing are the key. In big data's test, test engineers use commodity clusters and other supporting components to verify that TB-level data is processed successfully. Big data test can be roughly divided into several steps: data preparation and verification, output verification, data sampling and automation.

2. The development advantages of big data test:

(1) unstructured data

The amount of data will grow by 800% over the next five years, of which 80% will be contributed by unstructured data. Quality analysis of unstructured data will provide intelligent data insight, which is often difficult to determine with data warehouse facilities and other traditional business intelligence tools. Because unstructured data is usually large and unavailable, it can be mined for business benefits. Accurate data will help enterprises analyze their business competition and focus on their weaknesses in order to enhance their strength.

(2) Market strategy

Nowadays, companies are keen to take advantage of big data's advantages to plan their digital marketing strategies. With the development of web technology, it is easier for enterprises to collect large amounts of data based on user behavior and history. They can convert this data into a compelling, personalized experience for each customer who comes to the site. The big data test will help companies make better decisions by optimizing and predicting sexual behavior goals.

(3) optimize data quality.

Due to big data's poor quality, the enterprise loses 8.2 million to 100m US dollars a year. Nowadays, most enterprises have adopted the quality strategy to identify the bad data from the good data, but the loss is still great. The big data test helps reduce such losses by distinguishing valuable data from semi-structured and unstructured data heaps. It will help companies significantly improve customer service, make better business decisions and increase revenue.

3. The development dilemma of big data test.

(1) do a good job of quality analysis

Data is the lifeline of an organization and is growing every day. Today, enterprises are facing challenges in terms of the amount, type and source of data. Structured business data is supplemented by unstructured and semi-structured data from social media and other third parties. Finding basic data from such a large amount of data is a real challenge for enterprises, and quality analysis is the only choice.

(2) testing a large amount of data

Large size and heterogeneity are the biggest challenges in testing large amounts of data. Today, enterprises must store PB or EB data extracted from a variety of online and offline sources for day-to-day business. Testers need to audit large amounts of data to ensure that they are suitable for business use. How to store and prepare test cases for inconsistent large data? And because the amount of data is too large, it is impossible to carry out full capacity testing.

(3) big data tester

Big data testing technology is developing, and everyone is trying to understand and deal with big data's algorithm. At present, big data testers can be said to be scarce resources. Big data testers need to thoroughly understand the components of big data's ecosystem. Today, testers understand that they must consider the meaning behind the parameters of conventional automated testing and manual testing. Big data, with his unexpected format, may lead to problems that automated test cases cannot understand. Creating automated test cases for such a big data pool requires expertise and coordination among team members. The test team should coordinate with the development team and marketing team to understand the data extraction, data filtering, and pre-and post-processing algorithms for different resources. As there are many fully automated testing tools on the market for big data verification, testers must inevitably have the required skill sets and make use of big data technologies such as Hadoop.

This is the end of the development and dilemma of big data testing. This also provides some thoughts for the vast number of technical practitioners, and now the IT technology iteration is updated too fast. If you want to be invincible in the fierce competition in the workplace, you need to maintain a continuous learning attitude and master the most cutting-edge technology and knowledge. I hope you can seize the opportunity and have a better development prospect.

This is the end of the development and dilemma analysis of how to carry out big data test. I hope the above content can be helpful to you and learn more knowledge. If you think the article is good, you can share it for more people to see.

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